This script merges CalMAPPER activity data with treatment polygons. This is a necessary step before analyzing prescribed fire data in CalMAPPER.
calmapper_dir <- fs::dir_ls(ref_path, recurse = T, glob = '*CalMAPPER', type = 'directory')
calmapper_files <- fs::dir_ls(calmapper_dir, recurse = T, glob = '*.gdb')
if(length(calmapper_files) > 1){
stop( "There's more than one CalMAPPER .gdb file. script assumes only 1.")
}
st_layers(calmapper_files)
## Driver: OpenFileGDB
## Available layers:
## layer_name geometry_type features fields
## 1 CMDash_ProjectTreatments Multi Polygon 1560 21
## 2 CMDash_TreatmentPols Multi Polygon 2784 23
## 3 CMDash_TreatmentLines Multi Line String 23 22
## 4 CMDash_TreatmentPnts Multi Point 136 20
## 5 CMDash_Activities NA 20439 38
## 6 CMDash_Metadata NA 1 7
## crs_name
## 1 WGS 84 / Pseudo-Mercator
## 2 WGS 84 / Pseudo-Mercator
## 3 WGS 84 / Pseudo-Mercator
## 4 WGS 84 / Pseudo-Mercator
## 5 <NA>
## 6 <NA>
act <- st_read(calmapper_files, layer = 'CMDash_Activities')
## Reading layer `CMDash_Activities' from data source
## `C:\Users\ctubbesi\OneDrive - California Air Resources Board\Documents\Reference data\CALFIRE spatial data\CalMAPPER\CALFIRE_FuelReductionProjects_2023\CALFIRE_FuelReductionProjects.gdb'
## using driver `OpenFileGDB'
## Warning: no simple feature geometries present: returning a data.frame or tbl_df
trt <- st_read(calmapper_files, layer = 'CMDash_TreatmentPols')
## Reading layer `CMDash_TreatmentPols' from data source
## `C:\Users\ctubbesi\OneDrive - California Air Resources Board\Documents\Reference data\CALFIRE spatial data\CalMAPPER\CALFIRE_FuelReductionProjects_2023\CALFIRE_FuelReductionProjects.gdb'
## using driver `OpenFileGDB'
## Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, : GDAL
## Message 1: organizePolygons() received a polygon with more than 100 parts. The
## processing may be really slow. You can skip the processing by setting
## METHOD=SKIP, or only make it analyze counter-clock wise parts by setting
## METHOD=ONLY_CCW if you can assume that the outline of holes is counter-clock
## wise defined
## Simple feature collection with 2784 features and 23 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13842530 ymin: 3842624 xmax: -12941530 ymax: 5161286
## Projected CRS: WGS 84 / Pseudo-Mercator
act <- act %>%
filter(!ACTIVITY_DESCRIPTION %in% c("GIS Validation", "Education Outreach", "Project Administration", "Planning Meeting", "Public Contacts", "Water Site Development"))
act_sf <- right_join(trt %>% select(PROJECT_ID, TREATMENT_ID),
act)
## Joining with `by = join_by(PROJECT_ID, TREATMENT_ID)`
act_sf %>%
st_drop_geometry() %>%
group_by(ACTIVITY_DESCRIPTION) %>%
count() %>%
print(n=50)
## # A tibble: 47 × 2
## # Groups: ACTIVITY_DESCRIPTION [47]
## ACTIVITY_DESCRIPTION n
## <chr> <int>
## 1 Air Curtain Burner 13
## 2 Biomass Removal (Bone Dry Tons) 85
## 3 Boundary Mapping 43
## 4 Broadcast Burn 868
## 5 Chaining 100
## 6 Chipping 2216
## 7 Commercial Thinning (Cable Yarding) 20
## 8 Commercial Thinning (Tractor Yarding) 42
## 9 Crushing 51
## 10 Cultural Burning 5
## 11 Dozer Line 35
## 12 Environmental Review 110
## 13 Erosion Control 12
## 14 Follow up - Herbicide 58
## 15 Follow up - Other 6
## 16 Follow up - Slash disposal 177
## 17 Fuel Break (Shaded) 55
## 18 Grazing 52
## 19 Hand Line 108
## 20 Herbicide (Post-Treatment) 43
## 21 Herbicide (Pre-Treatment) 17
## 22 Invasive Plant Removal 7
## 23 Land Conservation 4
## 24 Limbing and Bucking 1204
## 25 Lop and Scatter 717
## 26 Mastication 1243
## 27 Pile Burning 1926
## 28 Piling (Manual) 2533
## 29 Piling (Mechanical) 375
## 30 Pruning 376
## 31 Public Meetings 21
## 32 RPF Supervision 86
## 33 Rangeland Mowing 61
## 34 Release - Herbicide 16
## 35 Release - Mechanical 99
## 36 Release - Other 10
## 37 Road Grading 6
## 38 Site Assessment 93
## 39 Site Preparation (CFIP) 71
## 40 Site Preparation (Manual) 77
## 41 Site Preparation (Mechanical) 7
## 42 Site Preparation (RxBurn) 187
## 43 Thinning 287
## 44 Thinning (Manual) 4400
## 45 Thinning (Mechanical) 442
## 46 Trees Felled (> 6in dbh) 1026
## 47 <NA> 1
act_sf_fire <-
act_sf %>%
filter(ACTIVITY_DESCRIPTION %in% c("Broadcast Burn", "Cultural Burning", "Pile Burning", "Site Preparation (RxBurn)"))
act_sf_fire <- act_sf_fire %>%
mutate(DURATION = as.Date(ACTIVITY_END) - as.Date(ACTIVITY_START)+1)
act_sf_fire <- act_sf_fire %>%
mutate(YEAR = year(ACTIVITY_END))
act_sf_fire %>%
select(ACTIVITY_START, ACTIVITY_END, DURATION, YEAR) %>%
head() %>%
nrow()
## [1] 6
save(act_sf_fire, file = "Rdata/CalMapper_activities_fire.Rdata")
write.csv(act_sf_fire %>% st_drop_geometry(), file = "~/Reference data/CALFIRE spatial data/CalMapper/activities_fire.csv", row.names = F)
write.csv(trt %>% st_drop_geometry(), file = "~/Reference data/CALFIRE spatial data/CalMapper/treatments_fire.csv", row.names = F)
act_broadcast <- act_sf_fire %>%
filter(ACTIVITY_DESCRIPTION == "Broadcast Burn")
trt %>%
st_drop_geometry() %>%
group_by(TREATMENT_OBJECTIVE) %>%
count()
## # A tibble: 5 × 2
## # Groups: TREATMENT_OBJECTIVE [5]
## TREATMENT_OBJECTIVE n
## <chr> <int>
## 1 Broadcast Burn 559
## 2 Forestland Stewardship 296
## 3 Fuel Break 215
## 4 Fuel Reduction 1542
## 5 Right of Way Clearance 172
trt_broadcast_complete <- trt %>%
filter(TREATMENT_OBJECTIVE == "Broadcast Burn") %>%
filter(ACTIVITY_STATUS == "Complete")
act_broadcast that
aren’t in trt – this shows whether the merge was
completeact_broadcast %>%
filter(!TREATMENT_ID %in% trt$TREATMENT_ID) %>%
nrow()
## [1] 0
act_broadcast %>%
filter(!TREATMENT_NAME %in% trt$TREATMENT_NAME) %>%
nrow()
## [1] 0
trt_broadcast_complete %>%
filter(!TREATMENT_ID %in% act_broadcast$TREATMENT_ID) %>%
mapview()
trt_broadcast_complete %>%
filter(!TREATMENT_NAME %in% act_broadcast$TREATMENT_NAME) %>%
nrow()
## [1] 7
missing_from_activities <- trt_broadcast_complete %>%
filter(!TREATMENT_ID %in% act_broadcast$TREATMENT_ID) %>%
select(CALMAPPER_ID, PROJECT_NAME, TREATMENT_NAME, TREATMENT_OBJECTIVE, PROJECT_STATUS, ACTIVITY_STATUS, PROJECT_START_DATE, PROJECT_END_DATE, TREATMENTAREA_ACRES) %>%
st_drop_geometry()
missing_from_activities %>%
summarize(missing_acres = sum(TREATMENTAREA_ACRES))
## missing_acres
## 1 653.99
write_excel_csv(missing_from_activities, "calmapper_trt_not_act.xls")
map <- mapview(list(trt_broadcast_complete, act_broadcast), col.regions=list("red","blue"),col=list("red","blue"))
map